Instructions to use moos124/code-reasoning-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moos124/code-reasoning-0.5b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("moos124/code-reasoning-0.5b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 2740, checkpoint
Browse files
last-checkpoint/adapter_model.safetensors
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last-checkpoint/optimizer.pt
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last-checkpoint/rng_state.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 0.
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"eval_steps": 500,
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"global_step":
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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| 2748 |
"mean_token_accuracy": 0.7586753875017166,
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| 2749 |
"num_tokens": 12681486.0,
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| 2750 |
"step": 2730
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}
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],
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| 2753 |
"logging_steps": 10,
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"attributes": {}
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}
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},
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"total_flos": 6.
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| 2771 |
"train_batch_size": 4,
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| 2772 |
"trial_name": null,
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| 2773 |
"trial_params": null
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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 0.5845333333333333,
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"eval_steps": 500,
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"global_step": 2740,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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| 2748 |
"mean_token_accuracy": 0.7586753875017166,
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| 2749 |
"num_tokens": 12681486.0,
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| 2750 |
"step": 2730
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| 2751 |
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},
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| 2752 |
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{
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| 2753 |
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"entropy": 0.9214616276323795,
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| 2754 |
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"epoch": 0.5845333333333333,
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| 2755 |
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"grad_norm": 0.2720118761062622,
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| 2756 |
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"learning_rate": 8.321488585053285e-05,
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| 2757 |
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"loss": 1.0133691787719727,
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| 2758 |
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"mean_token_accuracy": 0.7658515647053719,
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| 2759 |
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"num_tokens": 12728360.0,
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| 2760 |
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"step": 2740
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| 2761 |
}
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| 2762 |
],
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| 2763 |
"logging_steps": 10,
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| 2777 |
"attributes": {}
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| 2778 |
}
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| 2779 |
},
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| 2780 |
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"total_flos": 6.033431656914125e+16,
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| 2781 |
"train_batch_size": 4,
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| 2782 |
"trial_name": null,
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| 2783 |
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